ubuntu+anaconda

1、下载anaconda

查看ubuntu是32位还是64位

命令：

 uname -m

如果显示i686,你安装了32位操作系统

如果显示 x86_64，你安装了64位操作系统

 uname -a   查看更多内容

　　

2、安装

bash  Anaconda2-4.2.-Linux-x86_64.sh

一路默认，然后配置环境

sudo gedit ~/.bashrc
export PATH="/home/nxp/anaconda2/bin:$PATH"  anaconda安装
source ~/.bashrc

3、验证

conda list

没有错误，则配置好了。

换源，下载速度加快

pip

win下文件路径：C:\Users\你的用户名\pip\pip.ini

linux下文件路径：~/.pip/pip.conf

内容：

[global]
index-url = https://pypi.tuna.tsinghua.edu.cn/simple
[install]
trusted-host=pypi.tuna.tsinghua.edu.cn

---

conda

命令行下两条命令即可

conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/

conda config --set show_channel_urls yes

创建环境

conda create -n testcaffe python=2.7

进入环境

source activate testcaffe

退出环境

source deactivate

下载caffe

添加anaconda的python路径，注释掉系统本身自带的python路径。

注释掉系统目录

#PYTHON_INCLUDE := /usr/include/python2.7 \
#/usr/lib/python2.7/dist-packages/numpy/core/include

打开Anaconda目录
# Anaconda Python distribution is quite popular. Include path:
# Verify anaconda location, sometimes it's in root.
ANACONDA_HOME := $(HOME)/anaconda2
PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
$(ANACONDA_HOME)/include/python2.7 \
$(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include

# Uncomment to use Python 3 (default is Python 2)
# PYTHON_LIBRARIES := boost_python3 python3.5m
# PYTHON_INCLUDE := /usr/include/python3.5m \
# /usr/lib/python3.5/dist-packages/numpy/core/include

# We need to be able to find libpythonX.X.so or .dylib.
#PYTHON_LIB := /usr/lib
PYTHON_LIB := $(ANACONDA_HOME)/lib

看看PYTHONPATH变量，把当前caffe中python路径 加到~/.bashrc中

编译caffe

make all -j8
make pycaffe
make test -j8
make runtest -j8

也许你在编译runtest的时候，会报这样的错误：

.build_release/test/test_all.testbin: error while loading shared libraries: libhdf5.so.10: cannot open shared object file: No such file or directory

进入/usr/lib/x86_64-linux-gnu看一下，你的libhdf5.so.x中的那个x是多少，比如我的是libhdf5.so.7

# cd /usr/lib/x86_64-linux-gnu
# sudo ln -s libhdf5.so.7 libhdf5.so.10
# sudo ln -s libhdf5_hl.so.7 libhdf5_hl.so.10
# sudo ldconfig

 

安装protobuf

conda install protobuf

运行

jupyter notebook

终端直接运行jupyter notebook